Pupil size as a soft biometrics for age and gender classification

Cascone, Lucia; Medaglia, Carlo Maria; Nappi, Michele; Narducci, Fabio

doi:10.1016/j.patrec.2020.10.009

Cited by 15 publications

(14 citation statements)

References 15 publications

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“…In addition, Cascone et al (2020), offered pupil size as a soft biometric for identifying age and gender. They conducted comprehensive research with the goal of proving that pupil size and dilation over time may be used to identify individuals by age and gender.…”

Section: Literatures Reviewmentioning

confidence: 99%

“…Various categorization methods are discussed in each article on gender recognition. There are several categorization methods used, including C4.5 Decision Trees, KNNs, Adaboosts, Random Forests, Gini Indexs, SVMs, Multi-Layer Perceptions, and Optimized Incremental Reduced Error (J. E. Tapia & Perez, 2019)(Cascone et al, 2020) (Rattani et al, 2018;Sharanappa Gornale et al, 2020;.The objective of labeled dataset training is to obtain high performance on the test dataset after training a neural network on the training dataset, and the training dataset is typically separated into training and test datasets (and may also include a validation dataset). By comparing new images to the pretrained model, you can categorize them within the same data distribution space.…”

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confidence: 99%

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A Study of Gender Classification Techniques Based on Iris Images: A Deep Survey and Analysis

Hasan

Mstafa

2022

SJUOZ

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Gender classification is attractive in a range of applications, including surveillance and monitoring, corporate profiling, and human-computer interaction. Individuals' identities may be gleaned from information about their gender, which is a kind of soft biometric. Over the years, several methods for determining a person's gender have been devised. Some of the most well-known ones are based on physical characteristics like face, fingerprint, palmprint, DNA, ears, gait, and iris. On the other hand, facial features account for the vast majority of gender classification methods. Also, the iris is a significant biometric trait, because the iris, according to research, remains basically constant during an individual's life. Besides that, the iris is externally visible and is non-invasive to the user, which is important for practical applications. Furthermore, there are already high-quality methods for segmenting and encoding iris images, and the current methods facilitate selecting and extracting attribute vectors from iris textures. This study discusses several approaches to determining gender. The previous works of literature are briefly reviewed. Additionally, there are a variety of methodologies for different steps of gender classification. This study provides researchers with knowledge and analysis of the existing gender classification approaches. Also, it will assist researchers who are interested in this specific area, as well as highlight the gaps and challenges in the field, and finally provide suggestions and future paths for improvement.

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Section: Literatures Reviewmentioning

confidence: 99%

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confidence: 99%

A Study of Gender Classification Techniques Based on Iris Images: A Deep Survey and Analysis

Hasan

Mstafa

2022

SJUOZ

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“…Several techniques were proposed for gender classification that depend on the type of data in which the technique was applied (e.g., image, speech, text, biometrics) [14][15][16][17][18][19]. Our goal is to recognize the gender from text, more specifically, from the username.…”

Section: Introductionmentioning

confidence: 99%

Using Dynamic Pruned N-Gram Model for Identifying the Gender of the User

et al. 2022

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Organizations analyze customers’ personal data to understand and model their behavior. Identifying customers’ gender is a significant factor in analyzing markets that help plan the promotional campaigns, determine target customers and provide relevant offers. Several techniques were developed to analyze different types of data, including text, image, speech, and biometrics, to identify the gender of the user. The method of synthesis of the profile name differs from one customer to another. Using numerical substitutions of specific letters, known as Leet language, impedes the gender identification task. Moreover, using acronyms, misspellings, and adjacent names impose additional challenges. Towards this goal, this work uses the customers’ profile names associated with submitted reviews to recognize the customers’ gender. First, we create datasets of profile names extracted from the customers’ reviews. Secondly, we introduce a dynamic pruned n-gram model for identifying the gender of the user. It starts with data segmentation to handle adjacent parts, followed by data conversion and cleaning to fix the use of Leet language. Feature selection through a dynamic pruned n-gram model is the next step with the recurrent misspelling correction using fuzzy matching. We evaluate the proposed approach on the real data collected from active web resources. The obtained results demonstrate its validity and reliability.

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“…Several biometric signs have been used for gender recognition until today. Face ( (Rwigema et al, 2021), (Chandra Sekhar Reddy et al, 2020), (Swaminathan et al, 2020), (Chen et al, 2019), (Duan et al, 2018)), pupil size ( (Cascone et al, 2020)), handwriting ( (Gattal et al, 2018)), speech ((Qawaqneh et al, 2017), (Barkana & Zhou, 2015), (Kaya et al, 2017)), nose shape ( (Lv et al, 2019)), and social media recordings ( (Reynaldo et al, 2019)) are the mediums used in gender recognition.…”

Section: Introductionmentioning

confidence: 99%

Gait Data for Efficient Gender Recognition

Şentürk¹

2022

European Journal of Science and Technology

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Biometric recognition applications have been frequently used nowadays mostly because of reliability and ease of use for automated detection. There are many applications based on eyes, face, fingerprint, and voice for authentication and gender classification. In this paper, we focused on gender detection using the features of the steps of people. A different biometric sign has been investigated. Gait analyses were examined to determine the gender information of the people. Basic parameters like speed, variability, and symmetry of a gait, its several temporary, spatial, and height parameters, which were obtained via Physilog 5 sensor, were used in the analysis. A 321-D feature vector was comprised based on these features and an Artificial Neural Networks (ANN) model was trained with them. 95.83% accuracy was obtained. The experimental results show the success of the proposed ANN-based gait analysis system against the stateof-the-art for gender classification.

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Pupil size as a soft biometrics for age and gender classification

Cited by 15 publications

References 15 publications

A Study of Gender Classification Techniques Based on Iris Images: A Deep Survey and Analysis

A Study of Gender Classification Techniques Based on Iris Images: A Deep Survey and Analysis

Using Dynamic Pruned N-Gram Model for Identifying the Gender of the User

Gait Data for Efficient Gender Recognition

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